Background:Previous studies have shown that population susceptibility to non-optimum temperatures has changed over time, but little is known about the related time-varying factors that underlie the changes.Objective:Our objective was to investigate the changing population susceptibility to non-optimum temperatures in 47 prefectures of Japan over four decades from 1972 to 2012, addressing three aspects: minimum mortality temperature (MMT) and heat- and cold-related mortality risks. In addition, we aimed to examine how these aspects of susceptibility were associated with climate, demographic, and socioeconomic variables.Methods:We first used a two-stage time-series design with a time-varying distributed lag nonlinear model and multivariate meta-analysis to estimate the time-varying MMT, heat- and cold-related mortality risks. We then applied linear mixed effects models to investigate the association between each of the three time-varying aspects of susceptibility and various time-varying factors.Results:MMT increased from 23.2 [95% confidence interval (CI): 23, 23.6] to 28.7 (27.0, 29.7) °C. Heat-related mortality risk [relative risk (RR) for the 99th percentile of temperature vs. the MMT] decreased from 1.18 (1.15, 1.21) to 1.01 (0.98, 1.04). Cold-related mortality risk (RR for the first percentile vs. the MMT) generally decreased from 1.48 (1.41, 1.54) to 1.35 (1.32, 1.40), with the exception of a few eastern prefectures that showed increased risk. The changing patterns in all three aspects differed by region, sex, and causes of death. Higher mean temperature was associated (p<0.01) with lower heat risk, whereas higher humidity was associated with higher cold risk. A higher percentage of elderly people was associated with a higher cold risk, whereas higher economic strength of the prefecture was related to lower cold risk.Conclusions:Population susceptibility to heat has decreased over the last four decades in Japan. Susceptibility to cold has decreased overall except for several eastern prefectures where it has either increased or remained unchanged. Certain climate, demographic, and socioeconomic factors explored in the current study might underlie this changing susceptibility. https://doi.org/10.1289/EHP2546

Aims We aimed to investigate the heterogeneity of seasonal suicide patterns among multiple geographically, demographically and socioeconomically diverse populations. Methods Weekly time-series data of suicide counts for 354 communities in 12 countries during 1986–2016 were analysed. Two-stage analysis was performed. In the first stage, a generalised linear model, including cyclic splines, was used to estimate seasonal patterns of suicide for each community. In the second stage, the community-specific seasonal patterns were combined for each country using meta-regression. In addition, the community-specific seasonal patterns were regressed onto community-level socioeconomic, demographic and environmental indicators using meta-regression. Results We observed seasonal patterns in suicide, with the counts peaking in spring and declining to a trough in winter in most of the countries. However, the shape of seasonal patterns varied among countries from bimodal to unimodal seasonality. The amplitude of seasonal patterns (i.e. the peak/trough relative risk) also varied from 1.47 (95% confidence interval [CI]: 1.33–1.62) to 1.05 (95% CI: 1.01–1.1) among 12 countries. The subgroup difference in the seasonal pattern also varied over countries. In some countries, larger amplitude was shown for females and for the elderly population (≥65 years of age) than for males and for younger people, respectively. The subperiod difference also varied; some countries showed increasing seasonality while others showed a decrease or little change. Finally, the amplitude was larger for communities with colder climates, higher proportions of elderly people and lower unemployment rates (p-values < 0.05). Conclusions Despite the common features of a spring peak and a winter trough, seasonal suicide patterns were largely heterogeneous in shape, amplitude, subgroup differences and temporal changes among different populations, as influenced by climate, demographic and socioeconomic conditions. Our findings may help elucidate the underlying mechanisms of seasonal suicide patterns and aid in improving the design of population-specific suicide prevention programmes based on these patterns.

Figure 1: Our system predicts the lower-body pose of the user from sparse tracking signals of the head, hands, and pelvis for a wide range of actions using off-the-shelf VR devices.

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